A robust deep learning approach for photovoltaic power forecasting based on feature selection and variational mode decomposition

Authors

  • Mokhtar Ali
    Laboratory of Telecommunication and Smart Systems (LTSS), Faculty of Science and Technology, University of Djelfa, PO Box 3117, Djelfa 17000, Algeria
  • Abdelkerim Souahlia
    Laboratory of Telecommunication and Smart Systems (LTSS), Faculty of Science and Technology, University of Djelfa, PO Box 3117, Djelfa 17000, Algeria
  • Abdelhalim Rabehi
    Laboratory of Telecommunication and Smart Systems (LTSS), Faculty of Science and Technology, University of Djelfa, PO Box 3117, Djelfa 17000, Algeria
  • Mawloud Guermoui
    Laboratory of Telecommunication and Smart Systems (LTSS), Faculty of Science and Technology, University of Djelfa, PO Box 3117, Djelfa 17000, Algeria; Unite de Recherche Appliquee en Energies Renouvelables, URAER, Centre de Developpement des Energies Renouvelables, CDER, 47133, Ghardaia, Algeria
  • Ali Teta
    Applied Automation and Industrial Diagnostics Laboratory (LAADI), Faculty of Sciences and Technology, University of Djelfa, 17000 DZ, Algeria
  • Imad Eddine Tibermacine
    Department of Computer, Control, and Management Engineering, Sapienza University of Rome, 00185, Rome, Italy
  • Abdelaziz Rabehi
    Laboratory of Telecommunication and Smart Systems (LTSS), Faculty of Science and Technology, University of Djelfa, PO Box 3117, Djelfa 17000, Algeria
  • Mohamed Benghanem
    Physics Department, Faculty of Science, Islamic University of Madinah, Madinah, 42351, Saudi Arabia

Keywords:

PV power, Renewable energy, Feature selection, Artificial Neural Networks, Forecasting

Abstract

Accurate forecasting of photovoltaic (PV) power is essential for effective grid integration and energy management, particularly in solar-rich regions such as Algeria. This study presents a robust forecasting framework that combines advanced feature selection techniques with deep learning architectures---namely MLP, GRU, LSTM, BiLSTM, and CNN---to enhance daily PV power prediction accuracy. Three feature selection methods---ReliefF, Minimum Correlation, and Minimum Redundancy Maximum Relevance (MRMR)---are employed to identify the most relevant input variables from a dataset collected in the Ghardaia region. Among the selected predictors, Global Solar Radiation (GSR) consistently proves to be the most influential. To further enhance model inputs, Variational Mode Decomposition (VMD) is applied to extract informative Intrinsic Mode Functions (IMFs) from the selected features. A comparative evaluation of the models indicates that recurrent neural networks, particularly GRU and LSTM, deliver superior performance across various metrics, including RMSE, MAE, nRMSE, nMAE, R², and the correlation coefficient. The GRU model achieves the best results, with an RMSE of 3.246 and an R² of 0.9550 using five IMFs. These findings highlight the effectiveness of integrating optimal feature selection, signal decomposition, and deep learning for reliable PV power forecasting. The proposed hybrid approach provides a practical and scalable solution for enhancing energy planning and operational efficiency in high-solar-potential regions.

Dimensions

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Published

2025-08-01

How to Cite

A robust deep learning approach for photovoltaic power forecasting based on feature selection and variational mode decomposition. (2025). Journal of the Nigerian Society of Physical Sciences, 7(3), 2795. https://doi.org/10.46481/jnsps.2025.2795

Issue

Volume 7, Issue 3, August 2025

Section

Computer Science
Copyright & Licensing

Copyright (c) 2025 Mokhtar Ali, Abdelkerim Souahlia, Abdelhalim Rabehi, Mawloud Guermoui, Ali Teta, Imad Eddine Tibermacine, Abdelaziz Rabehi, Mohamed Benghanem

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

A robust deep learning approach for photovoltaic power forecasting based on feature selection and variational mode decomposition. (2025). Journal of the Nigerian Society of Physical Sciences, 7(3), 2795. https://doi.org/10.46481/jnsps.2025.2795

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